Every year, novel NVIDIA GPU designs are introduced. This rapid architectural and
technological progression, coupled with a reluctance by manufacturers to disclose low-level …

Performing Deep Neural Network (DNN) computation on hardware accelerators efficiently is
challenging. Existing DNN frameworks and compilers often treat the DNN operators in a …

Half-precision matrix multiply has played a key role in the training of deep learning models.
The newly designed Nvidia Tensor Cores offer the native instructions for half-precision small …

This work presents CLTune, an auto-tuner for OpenCL kernels. It evaluates and tunes kernel
performance of a generic, user-defined search space of possible parameter-value …

The general matrix-matrix multiplication (GEMM) is the most important numerical kernel in
dense linear algebra, and is the key component for obtaining high performance in most …

Originally developed to support video games, graphics processor units (GPUs) are now
increasingly used for general-purpose (non-graphics) applications ranging from machine …

This work introduces CLBlast, an open-source BLAS library providing optimized OpenCL
routines to accelerate dense linear algebra for a wide variety of devices. It is targeted at …

To support massive number of parallel thread contexts, Graphics Processing Units (GPUs)
use a huge register file, which is responsible for a large fraction of GPU's total power and …

In this paper, we present an optimized implementation for single-precision Winograd
convolution on NVIDIA Volta and Turing GPUs. Compared with the state-of-the-art Winograd …

GPU implementations of HPC applications relying on finite difference methods can include
tens of kernels that are memory-bound. Kernel fusion can improve performance by reducing …